most citedAuto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization

6 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20231 cited

Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

Shanlin Sun, Thanh-Tung Le, Chenyu You +6

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR begin…

cs.CV20232 cited

Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution

Ming Cheng, Haoyu Ma, Qiufang Ma +7

Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, th…

eess.IV20231 cited

OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution

Xiaopeng Sun, Weiqi Li, Zhenyu Zhang +8

360° omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower an…

cs.CV2023

Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation

Xiangyi Yan, Junayed Naushad, Chenyu You +6

Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in…

cs.CV20226 cited

Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization

Zhengang Li, Mengshu Sun, Alec Lu +9

Vision transformers (ViTs) are emerging with significantly improved accuracy in computer vision tasks. However, their complex architecture and enormous computation/storage demand i…

cs.CV20223 cited

Training Your Sparse Neural Network Better with Any Mask

Ajay Jaiswal, Haoyu Ma, Tianlong Chen +2

Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable du…